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First difference or forward demeaning: Implications for the method of moments estimators

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  • Cheng Hsiao
  • Qiankun Zhou

Abstract

In this paper, we consider the method of moment estimation for dynamic panel models based on either forward demeaning (FOD) or first difference (FD) transformations to eliminate the individual-specific effects, using either all lags or one lag as instruments. We show that the Arellano–Bond-type generalized method of moment (GMM) based on FD is asymptotically biased of order \begin{equation}\sqrt{c}\end{equation}c using all lags or one lag as instruments where \begin{equation}c={{T} \over {N}}\lte{}\infty \end{equation}c=TN<∞ as N,T→∞. For GMM based on FOD, it is asymptotically biased of order \begin{equation}\sqrt{c}\end{equation}c when using all lags, but it is asymptotically unbiased when using only fixed number of lags as instruments. We also discuss these findings in light of the simple IV estimator. Monte Carlo simulations confirm our findings in this paper.

Suggested Citation

  • Cheng Hsiao & Qiankun Zhou, 2017. "First difference or forward demeaning: Implications for the method of moments estimators," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 883-897, October.
    • Handle: RePEc:taf:emetrv:v:36:y:2017:i:6-9:p:883-897
    DOI: 10.1080/07474938.2017.1307594
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    Citations

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    Cited by:

    1. Hsiao, Cheng & Zhou, Qiankun, 2018. "Jive For Panel Dynamic Simultaneous Equations Models," Econometric Theory, Cambridge University Press, vol. 34(6), pages 1325-1369, December.
    2. Zhang, Yonghui & Zhou, Qiankun, 2019. "Estimation for time-invariant effects in dynamic panel data models with application to income dynamics," Econometrics and Statistics, Elsevier, vol. 9(C), pages 62-77.
    3. Robert F. Phillips, 2022. "Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias," Papers 2212.14075, arXiv.org, revised Jul 2024.
    4. Wendler, Tobias & Töbelmann, Daniel & Günther, Jutta, 2021. "Natural resources and technology - on the mitigating effect of green tech," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242416, Verein für Socialpolitik / German Economic Association.
    5. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    6. Tobias Wendler & Daniel Töbelmann & Jutta Günther, 2019. "Natural resources and technology - on the mitigating effect of green tech," Bremen Papers on Economics & Innovation 1905, University of Bremen, Faculty of Business Studies and Economics.
    7. Hsiao, Cheng & Zhou, Qiankun, 2018. "Incidental parameters, initial conditions and sample size in statistical inference for dynamic panel data models," Journal of Econometrics, Elsevier, vol. 207(1), pages 114-128.
    8. Tobias Wendler, 2019. "About the Relationship Between Green Technology and Material Usage," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(3), pages 1383-1423, November.
    9. Shiyun Cao & Yonghui Zhang & Qiankun Zhou, 2021. "2SLS and IV Estimation of Dynamic Panel Models with Heterogeneous Trend," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1408-1431, December.
    10. Robert F. Phillips, 2019. "A Comparison of First-Difference and Forward Orthogonal Deviations GMM," Papers 1907.12880, arXiv.org.

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